This PhD thesis describes statistical methods for modelling space time phenomena. The methods were applied to data from the Danish marine monitoring program in the Kattegat, measured in the five-year period 1993-1997. The proposed model approaches are characterised as relatively simple methods, which can handle missing data values and utilize the spatial and temporal correlation in data. Modelling results can be used to improve reporting on the state of the marine environment in the Kattegat.

The thesis also focus on design of monitoring networks, from which geostatistics can be successfully applied. Existing design methods are reviewed, and based on these a new Bayesian geostatistical design approach is suggested. This focus on constructing monitoring networks which are efficient for computing spatial predictions, while taking the uncertainties of the parameters in the geostatistical model into account. Thus, it serves as a compromise between existing methods. The space-time model approaches and geostatistical design methods used in this thesis are generally applicable, i.e. with minor modifications they could equally well be applied within areas such as soil and air pollution.